Impact of guidelines for the management of minor head injury on the utilization and diagnostic yield of CT over two decades, using natural language processing in a large dataset

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Publication

Objectives We investigated the impact of clinical guidelines for the management of minor head injury on utilization and
diagnostic yield of head CT over two decades.
Methods Retrospective before-after study using multiple electronic health record data sources. Natural language processing
algorithms were developed to rapidly extract indication, Glasgow Coma Scale, and CT outcome from clinical records, creating
two datasets: one based on all head injury CTs from 1997 to 2009 (n = 9109), for which diagnostic yield of intracranial traumatic
findings was calculated. The second dataset (2009–2014) used both CT reports and clinical notes from the emergency department, enabling selection of minor head injury patients (n = 4554) and calculation of both CT utilization and diagnostic yield.
Additionally, we tested for significant changes in utilization and yield after guideline implementation in 2011, using chi-square
statistics and logistic regression.
Results The yield was initially nearly 60%, but in a decreasing trend dropped below 20% when CT became routinely used for
head trauma. Between 2009 and 2014, of 4554 minor head injury patients overall, 85.4% underwent head CT. After guideline
implementation in 2011, CT utilization significantly increased from 81.6 to 87.6% (p = 7 × 10−7
), while yield significantly
decreased from 12.2 to 9.6% (p = 0.029).
Conclusions The number of CTs performed for head trauma gradually increased over two decades, while the yield decreased. In 2011,
despite implementation of a guideline aiming to improve selective use of CT in minor head injury, utilization significantly increased.

Pons, E., Foks, K.A, Dippel, D.W.J, & Hunink, M.G.M. (2019). Impact of guidelines for the management of minor head injury on the utilization and diagnostic yield of CT over two decades, using natural language processing in a large dataset. European Radiology: journal of the European Congress of Radiology, 29(5), 2632–2640. doi:10.1007/s00330-018-5954-5